Skip to content
Projects
Groups
Snippets
Help
Loading...
Help
Contribute to GitLab
Sign in / Register
Toggle navigation
S
stable-diffusion-webui
Project
Project
Details
Activity
Cycle Analytics
Repository
Repository
Files
Commits
Branches
Tags
Contributors
Graph
Compare
Charts
Issues
0
Issues
0
List
Board
Labels
Milestones
Merge Requests
0
Merge Requests
0
CI / CD
CI / CD
Pipelines
Jobs
Schedules
Charts
Wiki
Wiki
Snippets
Snippets
Members
Members
Collapse sidebar
Close sidebar
Activity
Graph
Charts
Create a new issue
Jobs
Commits
Issue Boards
Open sidebar
Administrator
stable-diffusion-webui
Commits
4d5f1691
Commit
4d5f1691
authored
Nov 29, 2022
by
brkirch
Browse files
Options
Browse Files
Download
Email Patches
Plain Diff
Use devices.autocast instead of torch.autocast
parent
21effd62
Hide whitespace changes
Inline
Side-by-side
Showing
5 changed files
with
6 additions
and
11 deletions
+6
-11
hypernetwork.py
modules/hypernetworks/hypernetwork.py
+1
-1
interrogate.py
modules/interrogate.py
+1
-2
swinir_model.py
modules/swinir_model.py
+1
-5
dataset.py
modules/textual_inversion/dataset.py
+2
-2
textual_inversion.py
modules/textual_inversion/textual_inversion.py
+1
-1
No files found.
modules/hypernetworks/hypernetwork.py
View file @
4d5f1691
...
@@ -495,7 +495,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step,
...
@@ -495,7 +495,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step,
if
shared
.
state
.
interrupted
:
if
shared
.
state
.
interrupted
:
break
break
with
torch
.
autocast
(
"cuda"
):
with
devices
.
autocast
(
):
x
=
batch
.
latent_sample
.
to
(
devices
.
device
,
non_blocking
=
pin_memory
)
x
=
batch
.
latent_sample
.
to
(
devices
.
device
,
non_blocking
=
pin_memory
)
if
tag_drop_out
!=
0
or
shuffle_tags
:
if
tag_drop_out
!=
0
or
shuffle_tags
:
shared
.
sd_model
.
cond_stage_model
.
to
(
devices
.
device
)
shared
.
sd_model
.
cond_stage_model
.
to
(
devices
.
device
)
...
...
modules/interrogate.py
View file @
4d5f1691
...
@@ -148,8 +148,7 @@ class InterrogateModels:
...
@@ -148,8 +148,7 @@ class InterrogateModels:
clip_image
=
self
.
clip_preprocess
(
pil_image
)
.
unsqueeze
(
0
)
.
type
(
self
.
dtype
)
.
to
(
devices
.
device_interrogate
)
clip_image
=
self
.
clip_preprocess
(
pil_image
)
.
unsqueeze
(
0
)
.
type
(
self
.
dtype
)
.
to
(
devices
.
device_interrogate
)
precision_scope
=
torch
.
autocast
if
shared
.
cmd_opts
.
precision
==
"autocast"
else
contextlib
.
nullcontext
with
torch
.
no_grad
(),
devices
.
autocast
():
with
torch
.
no_grad
(),
precision_scope
(
"cuda"
):
image_features
=
self
.
clip_model
.
encode_image
(
clip_image
)
.
type
(
self
.
dtype
)
image_features
=
self
.
clip_model
.
encode_image
(
clip_image
)
.
type
(
self
.
dtype
)
image_features
/=
image_features
.
norm
(
dim
=-
1
,
keepdim
=
True
)
image_features
/=
image_features
.
norm
(
dim
=-
1
,
keepdim
=
True
)
...
...
modules/swinir_model.py
View file @
4d5f1691
...
@@ -13,10 +13,6 @@ from modules.swinir_model_arch import SwinIR as net
...
@@ -13,10 +13,6 @@ from modules.swinir_model_arch import SwinIR as net
from
modules.swinir_model_arch_v2
import
Swin2SR
as
net2
from
modules.swinir_model_arch_v2
import
Swin2SR
as
net2
from
modules.upscaler
import
Upscaler
,
UpscalerData
from
modules.upscaler
import
Upscaler
,
UpscalerData
precision_scope
=
(
torch
.
autocast
if
cmd_opts
.
precision
==
"autocast"
else
contextlib
.
nullcontext
)
class
UpscalerSwinIR
(
Upscaler
):
class
UpscalerSwinIR
(
Upscaler
):
def
__init__
(
self
,
dirname
):
def
__init__
(
self
,
dirname
):
...
@@ -112,7 +108,7 @@ def upscale(
...
@@ -112,7 +108,7 @@ def upscale(
img
=
np
.
moveaxis
(
img
,
2
,
0
)
/
255
img
=
np
.
moveaxis
(
img
,
2
,
0
)
/
255
img
=
torch
.
from_numpy
(
img
)
.
float
()
img
=
torch
.
from_numpy
(
img
)
.
float
()
img
=
img
.
unsqueeze
(
0
)
.
to
(
devices
.
device_swinir
)
img
=
img
.
unsqueeze
(
0
)
.
to
(
devices
.
device_swinir
)
with
torch
.
no_grad
(),
precision_scope
(
"cuda"
):
with
torch
.
no_grad
(),
devices
.
autocast
(
):
_
,
_
,
h_old
,
w_old
=
img
.
size
()
_
,
_
,
h_old
,
w_old
=
img
.
size
()
h_pad
=
(
h_old
//
window_size
+
1
)
*
window_size
-
h_old
h_pad
=
(
h_old
//
window_size
+
1
)
*
window_size
-
h_old
w_pad
=
(
w_old
//
window_size
+
1
)
*
window_size
-
w_old
w_pad
=
(
w_old
//
window_size
+
1
)
*
window_size
-
w_old
...
...
modules/textual_inversion/dataset.py
View file @
4d5f1691
...
@@ -82,7 +82,7 @@ class PersonalizedBase(Dataset):
...
@@ -82,7 +82,7 @@ class PersonalizedBase(Dataset):
torchdata
=
torch
.
from_numpy
(
npimage
)
.
permute
(
2
,
0
,
1
)
.
to
(
device
=
device
,
dtype
=
torch
.
float32
)
torchdata
=
torch
.
from_numpy
(
npimage
)
.
permute
(
2
,
0
,
1
)
.
to
(
device
=
device
,
dtype
=
torch
.
float32
)
latent_sample
=
None
latent_sample
=
None
with
torch
.
autocast
(
"cuda"
):
with
devices
.
autocast
(
):
latent_dist
=
model
.
encode_first_stage
(
torchdata
.
unsqueeze
(
dim
=
0
))
latent_dist
=
model
.
encode_first_stage
(
torchdata
.
unsqueeze
(
dim
=
0
))
if
latent_sampling_method
==
"once"
or
(
latent_sampling_method
==
"deterministic"
and
not
isinstance
(
latent_dist
,
DiagonalGaussianDistribution
)):
if
latent_sampling_method
==
"once"
or
(
latent_sampling_method
==
"deterministic"
and
not
isinstance
(
latent_dist
,
DiagonalGaussianDistribution
)):
...
@@ -101,7 +101,7 @@ class PersonalizedBase(Dataset):
...
@@ -101,7 +101,7 @@ class PersonalizedBase(Dataset):
entry
.
cond_text
=
self
.
create_text
(
filename_text
)
entry
.
cond_text
=
self
.
create_text
(
filename_text
)
if
include_cond
and
not
(
self
.
tag_drop_out
!=
0
or
self
.
shuffle_tags
):
if
include_cond
and
not
(
self
.
tag_drop_out
!=
0
or
self
.
shuffle_tags
):
with
torch
.
autocast
(
"cuda"
):
with
devices
.
autocast
(
):
entry
.
cond
=
cond_model
([
entry
.
cond_text
])
.
to
(
devices
.
cpu
)
.
squeeze
(
0
)
entry
.
cond
=
cond_model
([
entry
.
cond_text
])
.
to
(
devices
.
cpu
)
.
squeeze
(
0
)
self
.
dataset
.
append
(
entry
)
self
.
dataset
.
append
(
entry
)
...
...
modules/textual_inversion/textual_inversion.py
View file @
4d5f1691
...
@@ -316,7 +316,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
...
@@ -316,7 +316,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
if
shared
.
state
.
interrupted
:
if
shared
.
state
.
interrupted
:
break
break
with
torch
.
autocast
(
"cuda"
):
with
devices
.
autocast
(
):
# c = stack_conds(batch.cond).to(devices.device)
# c = stack_conds(batch.cond).to(devices.device)
# mask = torch.tensor(batch.emb_index).to(devices.device, non_blocking=pin_memory)
# mask = torch.tensor(batch.emb_index).to(devices.device, non_blocking=pin_memory)
# print(mask)
# print(mask)
...
...
Write
Preview
Markdown
is supported
0%
Try again
or
attach a new file
Attach a file
Cancel
You are about to add
0
people
to the discussion. Proceed with caution.
Finish editing this message first!
Cancel
Please
register
or
sign in
to comment